First we demonstrate the computational advantages of the proposed Hessian approximation, and then that our sampling-based training leads to well calibrated uncertainties that can be used for ood detection, data imputation and semi-supervised learning. For all the downstream tasks we consider the following baselines: ae [1]} with constant and learned variance, vae [2]}, [3]}, Monte-Carlo dropout ae [4]} and Ensembles of ae [5]}. We extend StochMan[6]} with the Hessian backpropagation for the approximate and mixed diagonals. The training code is implemented in PyTorch and available\(^2\)2 https://github.com/FrederikWarburg/LaplaceAE. sec:experimentappendix provides more details on the experimental setup.
